Generally speaking, a sensor is a device that receives a signal or stimulus and responds with an electrical signal, while a transducer is a converter of one type of energy into another. In practice, however, the terms are often used interchangeably. Sensors and their associated circuits are used to measure various physical properties, such as temperature, force, pressure, flow, position, light intensity, etc. These properties act as the stimulus to the sensor, and the sensor output is conditioned and processed to provide the corresponding measurement of the physical property.
Sensors do not operate by themselves. They are generally part of a larger system consisting of signal conditioners and various analog or digital signal processing circuits. The larger system could be a measurement system, data acquisition system, or process control system, for example.
Sensors are used in many devices and systems to provide information on the parameters being measured or to identify the states of control. Microprocessors can make smart sensors or devices a reality. With this added capability, it is possible for a smart sensor to directly communicate measurements to an instrument or a system. In recent years, the concept of computer networking has gradually migrated into the sensor community. Networking of transducers (sensors or actuators) in a system and communicating transducer information via digital means versus analog cabling facilitates distributed measurements and control. In other words, intelligence and control, which were traditionally centralized, are gradually migrating to the sensor level. They can provide flexibility, improve system performance, and ease system installation, upgrade, and maintenance. Thus, the trend in industry is moving toward distributed control with intelligent sensing architecture. New advancement towards the minimization, reducing the cost and power requirements have motivated the researchers towards wireless sensor network. In sensor networks, different factors demand flexible and robust time synchronization, while simultaneously are making it more difficult to achieve as compared to computer networks.
One of the most important aspects of a sensor measurement system is the degree to which you can correlate in time the data acquired from multiple channels. If your data is not appropriately correlated in time, or synchronized, then your analysis and conclusions from your test data are inaccurate. In sensor networks, different factors demands flexible and robust time synchronization, while simultaneously is making it more difficult to achieve as compared to computer networks. Some sensors are also battery constrained that they only wake up occasionally, take a reading, transmit it and return to sleep, which may also complicate the synchronization task. Multiple channel measurements of the same physical target usually require that the sensors' local clocks be synchronized in frequency and phase. In the case the sensors are located close to each other, e.g. in the same apparatus, they can have a common master clock signal wired from a master to slaves. Synchronization becomes more challenging when devices working at a distance from each other must also work in conjunction over a network. Because smart sensor nodes have their own local clock, these nodes do not share global time or master clock. This lack of a global clock is problematic for multiple channel applications. Even if two clocks were synchronized at setup of the system, there is no guarantee that they will stay in synchronization. This is why the process of synchronization is continuous. Several factors can cause two identical clocks to lose synchronization. Causes such as differences in temperature, the age of the clocks themselves, and the rate of frequency can all affect the quality of synchronization. It is because of these factors that a need for clock synchronization arose.
To address this issue, several time synchronization techniques have been proposed so far. Reference Broadcast Synchronization (RBS), Flooding Time Synchronization Protocol (FTSP), Timing-sync Protocol for Sensor Networks (TPSN), IEEE 1588 and Simple Network Time Protocol (SNTP) are among the well-known synchronization methods.
IEEE 1588 provides fault tolerant synchronization for different clocks along the same network by using the precision time protocol, or PTP. The time protocol synchronizes all clocks within a network by adjusting clocks to the highest quality clock. The Best Master Clock (BMC) algorithm determines which clock is the highest quality clock within the network. The BMC (grandmaster clock) then synchronizes all other clocks (slave clocks) in the network. If the BMC is removed from the network or is determined by the BMC algorithm to no longer be the highest quality clock, the algorithm then redefines what the new BMC is and adjusts all other clocks accordingly.
SNTP is used to synchronize the clocks of networked computer system during data transfer via internet. SNTP synchronizes a computer's system time with a server that has already been synchronized by a source such as a radio, satellite receiver or modem.
However, these synchronization techniques are not optimal for correlating in time the data acquired from multiple channels in a sensor measurement system. A further disadvantage is that they must be supported in an application platform used in a smart sensor. These shortcomings are emphasized in embedded wireless solutions.